1. Field of the Invention
The present invention relates to a wireless communication system, and more particularly, to MIMO communication system.
2. Description of the Prior Art
Multiple-Input Multiple-Output (MIMO) technology utilizes antenna array to receive and transmit signals, which increases channel capacity with present spectrum resource, prevents signal loss caused by multiple-path, and meanwhile increases communication coverage. The present wireless communication standards, such as IEEE 802.11n used by wireless local area network, IEEE 802.16 used by WiMax, and long term evolution (LTE) initiated by the 3rd Generation Partnership Project (3GPP) all utilize MIMO technology to increase transmission throughput. On the other hand, high order quadrature amplitude modulation (QAM) technology is widespread applied in the abovementioned wireless communication standards.
A NT×NR MIMO system has NT transmitted antennas and NR received antennas. A transmitted signal is represented as x=└x1, x2, . . . xj, . . . , xNT┘T, wherein xj indicates a signal transmitted by the jth transmitted antenna and corresponds to a symbol of QAM constellation, also called a constellation point. T indicates a transpose matrix. A received signal of the NT×NR MIMO system can be indicated as y=└y1, y2, . . . yj, . . . , yNR┘T wherein yj indicates a signal received by the jth received antenna. The received signal can be indicated as y=Hx+n, where H is a channel matrix of NR×NT, and n is additive white Gaussian noise (AWGN). Please refer to FIG. 1, which is a schematic diagram of a 4×4 MIMO system 10. In a transmitter of the MIMO system 10, a data stream goes through a QAM unit 102 for modulation, and is transformed to a transmitted signal x=[x1, x2, x3, x4]T. After the transmitted signal is processed to be a radio frequency (RF) signal, the RF signal is transmitted to channels by antennas T1-T4. After antennas R1-R4 of a receiver of the MIMO system 10 receives the RF signal, and the RF signal goes through the RF signal process for generating a received signal y=[y1, y2, y3, y4]T to a MIMO detector 104. As can be seem in FIG. 1, each received antennas R1-R4 receives a signal transmitted from the transmitted antennas T1-T4, causing interference between signals transmitted from different antennas. The MIMO detector 104 generates an estimated transmitted signal {circumflex over (x)}=[{circumflex over (x)}1, {circumflex over (x)}2, {circumflex over (x)}3, {circumflex over (x)}4]T according to the received signal y=[y1, y2, y3, y4]T, and the estimated transmitted signal is demodulated by a QAM unit 106 to generate a valid transmitted signal.
The prior art provides various MIMO detection methods. Following is a brief introduction. Linear MIMO detection method, such as zero-forcing (ZF) algorithm and minimum mean square error (MMSE) algorithm, performs inverse matrix operation for an estimated channel matrix to extract the transmitted signal. An operation of ZF algorithm can be regarded as a filter, which can remove inter-symbol interference (ISI), but enhance noise. On the other hand, MMSE algorithm cannot remove ISI completely, but does not enhance noise. Both methods are simple to be implemented, but have limited efficiency In addition, nonlinear MIMO detection method includes vertical bell laboratories layered space time (V-BLAST) algorithm, maximum likelihood (ML) algorithm, sphere decoding (SD) algorithm, etc. The V-BLAST algorithm utilizes QR decomposition of matrix operation for successive interference cancellation. Compared to ZF and MMSE algorithm, V-BLAST algorithm has better performance. The maximum likelihood (ML) algorithm compares a received symbol vector and symbol vectors in transmitted signal space one by one, to detect the most possible symbol vector for transmission. ML algorithm has the best performance, but has the most complexity of all the algorithms. SD algorithm reduces the number of candidate symbol vectors and the complexity according to the setting of a search range, and has optimal performance with ML algorithm. However, a channel effect affects a process of determining a search radius of SD algorithm under the condition of low signal-to-noise ratio (SNR), and increases the complexity.
Besides the abovementioned algorithms, some methods use N-dimension QAM constellation with multilevel structure for decreasing the search range, to reduce the complexity. A brief description of the multilevel structure of N-QAM constellation is as following. An N-QAM constellation can be recursive partitioned to L levels, where L=log4 N. A mean symbol vector set Sl at level l isSl={sil},i=1,2, . . . ,Nl,Nl=41−l×N. 
Please refer to FIG. 2, which is a diagram of 64-QAM constellation according to the prior art. 64-QAM constellation is partitioned into four sets according to the four quadrants of I-Q plane. Each 16-QAM constellation can be further partitioned into four sets 4-QAM constellation. The mean symbol set Sl at the first level (which is also the lowest level) of the 64-QAM constellation includes 64 symbols. A mean value of all the symbols in each 4-QAM constellation is indicated as a mean symbol, such as a star point. As a result, a mean symbol set S2={s12, s22, , , , , , s162} at the second level includes 16 mean symbols. In the mean symbol set S2 at the second level, a mean value of four mean symbols in the same quadrant is indicated as a second mean symbol, such as a square point. Likewise, a mean symbol set S3={s13, s23, s33, s43} at the third level includes four mean symbols. The mean symbol sil at the lth level is the average of four mean symbols coupled to the mean symbol sil at the (l−1)th level; the mean symbol sil is
      s    i    l    =            (                        ∑                                    s              j                              l                -                1                                      ∈                          S                              s                ,                i                                            l                -                1                                                                                    ⁢                  s          j                      l            -            1                              )        /    4.  
Ss,il−1 is a subset of a mean symbol set Sl−1, which includes four mean symbols coupled to the mean symbol sil. The transmitted signal of the NT×NR MIMO system is represented as xl=[x1l,x2l, . . . ,xNTl]T according to a transmitted signal space formed by the mean symbol set at the lth level, where x1l,x2l, . . . ,xNTl indicates symbol vectors transmitted from the 1, 2, . . . NT transmitted antennas, and the symbol vector transmitted from each antenna corresponds to a mean symbol in the mean symbol set Sl. As can be seen, the overall mean symbol vector set Xl transmitted from the NT×NR MIMO system isXl={xl,i},i=1,2, . . . ,NlNT.
Take 4×4 MIMO system 10 in FIG. 1 as an example. 64-QAM is utilized for performing signal modulation and forming a search space according the mean symbol set at the second level. Then, the mean symbol vector set X2 includes 164 different mean symbol vectors. A cluster Cl,i corresponding to a mean symbol vector xl,i includes 4NT mean symbol vectors at the (l−1)th level coupled to the mean symbol vector xl,i, which can be regarded as a subset of a mean symbol vector Xl−1. The cluster Cl,i isCl,i={xil−1|x1l−1∈Sσ,1l−1,x2l−1∈Sσ,2l−1, . . . ,xNTl−1∈Sσ,NTl−1}.
Assume that the received signal y is a result of the transmitted signal x passed through the channel matrix H, a mean symbol vector xl in the mean symbol vector set at the lth level has the minimum distance with the transmitted signal x. A Euclidean distance of an error vector between the transmitted signal x and the mean symbol vector xl passed through the channel is∥Hx−Hxl∥2=∥H(x−xl)∥2=(x−xl)HHHH(x−xl).
Since each channel of the MIMO system affects each other, the abovementioned equation can only confirm that HHH diagonal element is a positive real number. Therefore, ∥Hx−Hxl∥2 may not be proportion to ∥(x−xl)∥2. In other words, after the mean symbol vector xl which is the nearest to the transmitted signal x in the transmitted signal space are transmitted through the channel, the mean symbol vector xl may not be a mean symbol vector which is the nearest to the received signal y in the received signal space, and system error occurs.
The method published in “Depth-First and Breadth-First Search Based Multilevel SGA Algorithms for Near Optimal Symbol Detection in MIMO Systems” provides depth-first search or breadth-first search to extract possible transmitted signals by sequential Gaussian approximation (SGA) algorithm which uses ZF algorithm, causing performance degradation. On the other hand, U.S. Pat. No. 7,308,047 discloses a method for detecting the most similar signal to the transmitted signal in the received signal space without performing the inverse matrix operation of the channel matrix. However, the method disclosed by the U.S. Pat. No. 7,308,047 causes serious system error and error floor effect at low bit error rate (BER).
Although the abovementioned multilevel structure of the QAM technology for detecting transmitted signal can reduce the system complexity, a problem of system error is still needed to conquer, for enhance accuracy of the MIMO detection. The MIMO detection method has to be improved constantly for adapting an amount of data transmission, and meanwhile takes low complexity and high performance into account.